Sensor fault detection and identification using residual failure pattern recognition

ABSTRACT

Systems, methods, and apparatus for sensor fault detection and identification using residual failure pattern recognition are disclosed. In one or more embodiments, a method for sensor fault detection and identification for a vehicle comprises sensing, with sensors located on the vehicle, data. The method further comprises performing majority voting on the data for each of the types of data to generate a single voted value for each of the types of data. Also, the method comprises generating, for each of the types of data, estimated values by using some of the voted values. In addition, the method comprises generating residuals by comparing the estimated values to the voted values. Further, the method comprises analyzing a pattern of the residuals to determine which of the types of the data is erroneous to detect and identify a fault experienced by at least one of the sensors on the vehicle.

FIELD

The present disclosure relates to sensor fault detection andidentification. In particular, the present disclosure relates to sensorfault detection and identification using residual failure patternrecognition.

BACKGROUND

Currently, aircraft pitot tubes are prone to measurement failure due toicing blockage, heavy water ingestion, volcanic ash blockage, etc. Acommon mode pneumatic event (CMPE) occurs when a majority of the pitottubes on the airplane are blocked simultaneously. When a CMPE occurs,the display airspeed, which is displayed on the cockpit display, will becorrupted. In addition, the aircraft flight control system, which has acertain window of time of exposure to the corrupted airspeed, may causethe airplane to deviate from its optimal flight path.

In light of the foregoing, there is a need for an improved technologyfor sensor fault detection and identification of sensor measurementfailures (e.g., airspeed, angle of attack, acceleration, etc.) onaircraft.

SUMMARY

The present disclosure relates to a method, system, and apparatus forsensor fault detection and identification using residual failure patternrecognition. In one or more embodiments, a method for sensor faultdetection and identification for a vehicle comprises sensing, withsensors located on the vehicle, data. In one or more embodiments, thedata comprises m number types of data. In at least one embodiment, m isan integer greater than two. The method further comprises performing, byat least one processor, majority voting on the data for each of thetypes of data to generate a single voted value for each of the types ofdata. Also, the method comprises generating, by at least one processor,for each of the types of data, estimated values by using at least someof the voted values. In some embodiments, m number of estimated valuesare generated by using n number of the voted values, where n is equal tom minus one. In addition, the method comprises generating, by at leastone processor, residuals by comparing the estimated values to the votedvalues. Further, the method comprises analyzing, by at least oneprocessor, a pattern of the residuals to determine which of the types ofthe data is erroneous to detect and identify a fault experienced by atleast one of the sensors on the vehicle.

In one or more embodiments, the data comprises measurement data. In atleast one embodiment, the sensors comprise at least two (2) of: a pitottube, a pitot-static tube, a static port, a static tube, an angle ofattack (AOA) resolver, or an accelerometer. In some embodiments, thetypes of data comprise at least three (3) of: total pressure, staticpressure, angle of attack (AOA), or acceleration.

In at least one embodiment, the vehicle is an airborne vehicle, aterrestrial vehicle, or a marine vehicle. In some embodiments, theairborne vehicle is an airplane, an unmanned aerial vehicle (UAV), or ahelicopter.

In one or more embodiments, the method further comprises removing, by atleast one processor, known corruption effects from at least some of thedata. In some embodiments, the known corruption effects are removed byat least one processor by utilizing dynamic pressure, Mach number, angleof attack (AOA), flap position, gear position, excess thrust, and/orground effect terms.

In at least one embodiment, at least one processor generates theestimated values by utilizing at least one statistical filter. In someembodiments, at least one statistical filter is an extended Kalmanfilter (EKF).

In one or more embodiments, the method further comprises generating, byat least one processor, an alert signal indicating which of the types ofdata is erroneous and/or which type of the sensors are experiencing afailure. In some embodiments, the method further comprises generating avisual alert and/or an audible alert based on information contained inthe alert signal.

In at least one embodiment, the method further comprises generating, byat least one processor, a synthetic data signal indicating to usesynthetic data instead of erroneous sensed data.

In at least one embodiment, a system for sensor fault detection andidentification for a vehicle comprises sensors, located on the vehicle,to sense data. In one or more embodiments, the data comprises m numbertypes of data. In some embodiments, m is an integer greater than two.The system further comprises at least one processor configured toperform majority voting on the data for each of the types of data togenerate a single voted value for each of the types of data; to generatefor each of the types of data, m number of estimated values by using nnumber of the voted values, where n is equal to m minus one; to generateresiduals by comparing the estimated values to the voted values; and toanalyze a pattern of the residuals to determine which of the types ofthe data is erroneous to detect and identify a fault experienced by atleast one of the sensors on the vehicle.

In one or more embodiments, at least one processor is further configuredto remove known corruption effects from at least some of the data. In atleast one embodiment, at least one processor is further configured togenerate the estimated values by utilizing at least one statisticalfilter. In some embodiments, at least one statistical filter is anextended Kalman filter (EKF). In one or more embodiments, at least onprocessor is further configured to generate an alert signal indicatingwhich of the types of data is erroneous and/or which type of the sensorsare experiencing a failure.

In at least one embodiment, the system further comprises: at least oneindicator light to generate a visual alert based on informationcontained in the alert signal, at least one display to display a visualdisplay alert based on the information contained in the alert signal,and/or at least one speaker to generate an audio alert based on theinformation contained in the alert signal.

In one or more embodiments, at least one processor is further configuredto generate a synthetic data signal indicating to use synthetic datainstead of erroneous sensed data.

The features, functions, and advantages can be achieved independently invarious embodiments of the present disclosure or may be combined in yetother embodiments.

DRAWINGS

These and other features, aspects, and advantages of the presentdisclosure will become better understood with regard to the followingdescription, appended claims, and accompanying drawings where:

FIG. 1A is a diagram showing a vehicle (e.g., an airplane) that may beemployed by the disclosed system for sensor fault detection andidentification for a vehicle, where the vehicle is shown to comprisevarious different types of sensors, in accordance with at least oneembodiment of the present disclosure.

FIG. 1B is a diagram showing a vehicle (e.g., an airplane) that may beemployed by the disclosed system for sensor fault detection andidentification for a vehicle, where the vehicle is shown to comprise aplurality of units utilized for generating an alert, in accordance withat least one embodiment of the present disclosure.

FIG. 2A is a diagram showing an exemplary pitot tube that may beemployed for one of the sensors of the disclosed system for sensor faultdetection and identification for a vehicle, in accordance with at leastone embodiment of the present disclosure.

FIG. 2B is a diagram showing an exemplary static tube that may beemployed for one of the sensors of the disclosed system for sensor faultdetection and identification for a vehicle, in accordance with at leastone embodiment of the present disclosure.

FIG. 2C is a diagram showing an exemplary pitot-static tube that may beemployed for one of the sensors of the disclosed system for sensor faultdetection and identification for a vehicle, in accordance with at leastone embodiment of the present disclosure.

FIG. 3 is a block diagram showing the disclosed system for sensor faultdetection and identification for a vehicle, in accordance with at leastone embodiment of the present disclosure.

FIG. 4 is a block diagram showing details of the functionality of thesynthetic air data and common mode monitor (CMM) of FIG. 3, inaccordance with at least one embodiment of the present disclosure.

FIG. 5 shows equations for generating the estimated values utilized bythe disclosed system for sensor fault detection and identification for avehicle, in accordance with at least one embodiment of the presentdisclosure.

FIG. 6A shows exemplary graphs illustrating the sensed data (i.e.measured data) and the corresponding real values (i.e. truth values) forfour different types of sensor data, where none of the sensed data iserroneous, in accordance with at least one embodiment of the presentdisclosure.

FIG. 6B shows exemplary graphs illustrating the estimated values (i.e.synthetic values) generated by the disclosed system for sensor faultdetection and identification for a vehicle, where none of the senseddata is erroneous, in accordance with at least one embodiment of thepresent disclosure.

FIG. 7A shows exemplary graphs illustrating the sensed data (i.e.measured data) and the corresponding real values (i.e. truth values) forfour different types of sensor data, where one of the types of senseddata (i.e. total pressure (P_(T))) is erroneous, in accordance with atleast one embodiment of the present disclosure.

FIG. 7B shows exemplary graphs illustrating the estimated values (i.e.synthetic values) generated by the disclosed system for sensor faultdetection and identification for a vehicle, where one of the types ofsensed data (i.e. total pressure (P_(T))) is erroneous, in accordancewith at least one embodiment of the present disclosure.

FIG. 8 shows equations for the fault detection logic utilized by thedisclosed system for sensor fault detection and identification for avehicle, in accordance with at least one embodiment of the presentdisclosure.

FIG. 9 is a diagram showing how the residuals are generated by thedisclosed system for sensor fault detection and identification for avehicle, in accordance with at least one embodiment of the presentdisclosure.

FIG. 10 is a chart showing an exemplary expected pattern of residualsutilized by the disclosed system for sensor fault detection andidentification for a vehicle, in accordance with at least one embodimentof the present disclosure.

FIGS. 11A and 11B together form a flow chart showing the disclosedmethod for sensor fault detection and identification for a vehicle, inaccordance with at least one embodiment of the present disclosure.

DESCRIPTION

The methods and apparatus disclosed herein provide an operative systemfor sensor fault detection and identification using residual failurepattern recognition. In one or more embodiments, the system of thepresent disclosure allows for sensor fault detection and identificationby detecting erroneous sensor measurement data (e.g., total pressure,static pressure, angle of attack (AOA), and acceleration) produced byfailed sensors (e.g., pitot tubes, static ports, AOA resolvers, andaccelerometers) on the vehicle (e.g., aircraft).

In particular, the system of the present disclosure providesmulti-sensor fault detection and identification by using a statisticalfilter (e.g., an extended Kalman filter (EKF)) architecture.Specifically, an array of statistical filters (e.g., extended Kalmanfilters (EKFs)) is utilized to compute different synthetic parameters(e.g., estimated values) for sensor health monitoring. In addition, thesystem leverages a pattern of residual characteristics (e.g., residuals)to identify which type of sensor (e.g., pitot tube, static port, AOAresolver, or accelerometer) failure has occurred on the vehicle inreal-time.

The system of the present disclosure possesses a number of benefits.Firstly, the system has the ability to directly monitor the totalpressure (P_(T)) and static pressure (P_(S)) sensor measurements byusing the statistical filters' (e.g., EKFs') synthetic total pressure(P_(T)) and static pressure (P_(S)). Secondly, the system is able toprovide a timely sensor failure detection that allows for the aircraftflight control system to provide an accurate, reliable flight deckengine-indicating and crew-alert system (EICAS) warning message to thepilots about which failure in the system has occurred and to discouragethe pilots from performing an aggressive airplane maneuver. And,thirdly, the system is able to provide an accurate sensor failureidentification that allows for the aircraft flight control system tocontinue operation by switching over to use the synthetic version of theerroneous measurement.

In the following description, numerous details are set forth in order toprovide a more thorough description of the system. It will be apparent,however, to one skilled in the art, that the disclosed system may bepracticed without these specific details. In the other instances, wellknown features have not been described in detail, so as not tounnecessarily obscure the system.

Embodiments of the present disclosure may be described herein in termsof functional and/or logical components and various processing steps. Itshould be appreciated that such components may be realized by any numberof hardware, software, and/or firmware components configured to performthe specified functions. For example, an embodiment of the presentdisclosure may employ various integrated circuit components (e.g.,memory elements, digital signal processing elements, logic elements,look-up tables, or the like), which may carry out a variety of functionsunder the control of one or more processors, microprocessors, or othercontrol devices. In addition, those skilled in the art will appreciatethat embodiments of the present disclosure may be practiced inconjunction with other components, and that the systems described hereinare merely example embodiments of the present disclosure.

For the sake of brevity, conventional techniques and components relatedto sensors, and other functional aspects of the system (and theindividual operating components of the systems) may not be described indetail herein. Furthermore, the connecting lines shown in the variousfigures contained herein are intended to represent example functionalrelationships and/or physical couplings between the various elements. Itshould be noted that many alternative or additional functionalrelationships or physical connections may be present in one or moreembodiments of the present disclosure.

In various embodiments, the disclosed system for sensor fault detectionand identification is employed in an airplane. It should be noted thatthe disclosed system for sensor fault detection and identification maybe employed by other vehicles other than an airplane as disclosedherein. The following discussion is thus directed to airplanes withoutloss of generality.

FIG. 1A is a diagram 100 showing a vehicle (e.g., an airplane) 110 thatmay be employed by the disclosed system for sensor fault detection andidentification for a vehicle 110, where the vehicle 110 is shown tocomprise various different types of sensors, in accordance with at leastone embodiment of the present disclosure. In this figure, the vehicle110 is shown to be an airplane. However, in other embodiments, variousdifferent types of vehicles may be employed for the vehicle 110 of thedisclosed system. In one or more embodiments, the disclosed system mayemploy an airborne vehicle, a terrestrial vehicle, or a marine vehiclefor the vehicle 110. In one or more embodiments, various different typesof airborne vehicles may be employed for the vehicle 110 including, butnot limited to, an airplane (as shown in FIG. 1A), an unmanned aerialvehicle (UAV), or a helicopter.

Also in this figure, the vehicle 110 is shown to comprise a number ofdifferent types of sensors. These sensors include pitot tubes 120, astatic port 130, angle of attack (AOA) resolvers 140, and anaccelerometer 150. It should be noted that the pitot tubes 120, staticport 130, and AOA revolvers 140 are located on the surface of thevehicle (e.g., airplane) 110, and the accelerometer 150 is locatedinternal to the vehicle (e.g., airplane) 110. These sensors sensedifferent types of measurement data. For example, the pitot tubes 120sense total pressure (P_(T)), the static port 130 senses static pressure(P_(S)), the AOA resolvers 140 sense AOA (α), and the accelerometer 150senses acceleration (A_(N)).

In addition, it should be noted that, in one or more embodiments, thevehicle 110 may comprise more or less than the number of sensors asshown. For example, the vehicle 110 is shown to comprise three pitottubes 120. In other embodiments, the vehicle 110 may comprise more orless pitot tubes 120 than three pitot tubes 120 as is shown in FIG. 1A.

Additionally, it should be noted that the vehicle 110 may comprisedifferent types of sensors than the sensors as shown. As such, it shouldbe understood that the types of sensors shown in FIG. 1A are merelyexample types of sensors that may be employed by the disclosed system.It follows that when utilizing different types of sensors, differenttypes of sensor measurement data will be sensed. The disclosed systemmay operate utilizing different types of sensor measurement data thanthe types of sensor measurement data discussed herein.

FIG. 1B is a diagram 160 showing a vehicle (e.g., an airplane) 110 thatmay be employed by the disclosed system for sensor fault detection andidentification for a vehicle 110, where the vehicle 110 is shown tocomprise a plurality of units utilized for generating an alert, inaccordance with at least one embodiment of the present disclosure. Inthis figure, the vehicle 110 is shown to additionally comprise aprocessor(s) 170 connected via wire 180 to a speaker 190, a display 191,and an indicator light 192. The processor(s) 170 may be located withinthe electronics area (e.g., electronics bay) of the vehicle (e.g.,airplane) 110; and the speaker 190, display (e.g., a display screen)191, and indicator light 192 may be located within the driver's (e.g.,pilot's) area (e.g., cockpit) of the vehicle 110.

The vehicle 110 is also shown to additionally comprise a flight controlsystem 195. The flight control system 195 may be located within theelectronics area (e.g., electronics bay) of the vehicle (e.g., airplane)110. The flight control system 195 is connected to the processor(s) 170via wire 197.

It should be noted that in other embodiments, the processor(s) 170 maybe connected to the speaker 190, the display 191, and the indicatorlight 192 wirelessly. In addition, in other embodiments, the vehicle 110may comprise more than one processor(s) 170, more than one speaker 190,more than one display 191, and/or more than one indicator light 192 asis shown. Also, it should be noted that the sensors (e.g., pitot tubes120, a static port 130, angle of attack (AOA) resolvers 140, and anaccelerometer 150) may be connected to the processor(s) 170 via wireand/or wirelessly.

During operation of the disclosed system, while the vehicle isoperating, the processor(s) 170 receives data (e.g., sensor measurementdata) from the different types of sensors (e.g., pitot tubes 120, astatic port 130, angle of attack (AOA) resolvers 140, and anaccelerometer 150) on the vehicle 110. The processor(s) 170 analyzes thedata and determines whether one of the types of data (e.g., totalpressure (P_(T)), static pressure (P_(S)), AOA (α), or the acceleration(A_(N))) measured is erroneous. If the processor(s) 170 determines thatone of the types of data is erroneous, meaning the erroneous data'sassociated sensor has failed, the processor(s) 170 generates an alertsignal to alert the driver (e.g., pilot) of the erroneous sensormeasurement data and/or sensor failure. The alert signal indicates whichtype of data is erroneous (e.g., total pressure (P_(T)) measured data iserroneous) and/or indicates which type of the sensors is experiencing afailure (e.g., the pitot tubes 120 are experiencing a failure (e.g.,from being blocked by icing)).

After the processor(s) 170 has generated an alert signal, theprocessor(s) 170 transmits the alert signal via wire 180 (or wirelessly)to the speaker 190, the display 191, and/or the indicator light 192. Inone or more embodiments, once the speaker 190 receives the alert signal,the speaker 190 will produce an audio alert, which may be an alarm toneand/or a verbal alert message describing the specific type of erroneousdata and/or the specific type of sensor that has failed. In someembodiments, when the display 191 receives the alert signal, the display191 will display to the driver (e.g., a pilot) a visual display alert,which may be a visual alert symbol, color, and/or a textual alertmessage describing the specific type of erroneous data and/or thespecific type of sensor that has failed. In at least one embodiment,after the indicator light 192 receives the alert signal, the indicatorlight 192 will illuminate and/or change illumination color (e.g., changeits color to red) to visually alert the driver (e.g., a pilot) of thevehicle 110.

In addition, when the processor(s) 170 determines that one of the typesof data is erroneous, meaning the erroneous data's associated sensor hasfailed, the processor(s) 170 generates a synthetic data signalindicating to use synthetic data instead of the erroneous sensed data.After the processor(s) 170 has generated the synthetic data signal, theprocessor(s) 170 transmits the synthetic data signal via wire 197 (orwirelessly) to the flight control system 195. Once the flight controlsystem 195 receives the synthetic data signal, the flight control system195 will utilize the synthetic data instead of the erroneous sensed datafor its flight computations.

FIG. 2A is a diagram 200 showing an exemplary pitot tube 120 that may beemployed for one of the sensors of the disclosed system for sensor faultdetection and identification for a vehicle 110, in accordance with atleast one embodiment of the present disclosure. In this figure, anexample pitot tube 120 is shown. The pitot tube 120 in FIG. 2A is shownto have one opening on its end. Pitot pressure is obtained from thepitot tube 120. The pitot pressure is a measure of ram air pressure(i.e. the air pressure created by vehicle motion or the air ramming intothe tube), which under ideal conditions is equal to stagnation pressure(also referred to as total pressure (P_(T))). The pitot tube 120 may beemployed by the disclosed system to obtain total pressure (P_(T)).

The pitot tube 120 is most often located on the wing or front section ofan aircraft facing forward, where its opening is exposed to the relativewind. By situating the pitot tube 120 in such a location, the ram airpressure is more accurately measured since it will be less distorted bythe aircraft's structure. When airspeed increases, the ram air pressureis increased, which can be translated by the airspeed indicator.

FIG. 2B is a diagram 250 showing an exemplary static tube 210 that maybe employed for one of the sensors of the disclosed system for sensorfault detection and identification for a vehicle 110, in accordance withat least one embodiment of the present disclosure. In this figure, anexample static tube 210 is shown. The static tube 210 of FIG. 2B isshown to have two openings, which are located on the top side and bottomside of the static tube 210. The static tube 210 obtains static pressure(P_(S)) similar to a static port 130 (refer to FIG. 1A). The static tube210 and/or the static port 130 may be employed by the disclosed systemto obtain static pressure (P_(S)).

A static port 130 (refer to FIG. 1A) is most often a flush-mounted holeon the fuselage of an aircraft, and is located where it can access theair flow in a relatively undisturbed area. Some aircraft may have asingle static port, while others may have more than one. In situationswhere an aircraft has more than one static port 130, there is usuallyone located on each side of the fuselage. With this positioning, anaverage pressure can be taken, which allows for more accurate readingsin specific flight situations. An alternative static port 130 may belocated inside the cabin of the aircraft as a backup for when theexternal static port(s) 130 are blocked.

FIG. 2C is a diagram 230 showing an exemplary pitot-static tube 220 thatmay be employed for one of the sensors of the disclosed system forsensor fault detection and identification for a vehicle 110, inaccordance with at least one embodiment of the present disclosure. Inthis figure, an example pitot-static tube 220 is shown. The pitot-statictube 220 of FIG. 2C is shown to have three openings, which are locatedon the end of the pitot-static tube 220, and on the top side and bottomside of the pitot-static tube 220. A pitot-static tube 220 effectivelyintegrates static ports into a pitot probe. It also incorporates asecond coaxial tube (or tubes) with pressure sampling holes on the sidesof the probe, outside the direct airflow, to measure the static pressure(P_(S)). When the aircraft climbs, the static pressure (P_(S)) willdecrease. A pressure transducer 240 is used to measure the difference intotal pressure (P_(T)) and static pressure (P_(S)). The pitot-statictube 220 may be employed by the disclosed system to obtain totalpressure (P_(T)) and static pressure (P_(S)).

FIG. 3 is a block diagram 300 showing the disclosed system for sensorfault detection and identification for a vehicle 110, in accordance withat least one embodiment of the present disclosure. In this figure, data(i.e. sensor measurements) sensed by the different types of sensorslocated on the vehicle are shown. In particular, total pressure (P_(T))data 310 a sensed by the pitot tubes 120, static pressure (P_(S)) data310 b sensed by the static port 130, angle of attack (AOA) (α) data 310c sensed by the AOA resolvers 140, and acceleration (A_(N)) data 310 dsensed by the accelerometer 150 are shown.

It should be noted that some of the sensed data comprises data sensedfrom multiple sensors. For example, the total pressure (P_(T)) data 310a comprises data sensed by three separate pitot tubes 120 located on thevehicle (e.g., airplane) 110. In FIG. 3, three arrows are shown to beoutputted from the total pressure (P_(T)) data 310 a arrow to representthese three pieces of sensed data.

During operation, the sensed data for the total pressure (P_(T)) data310 a, static pressure (P_(S)) data 310 b, and angle of attack (AOA) (α)data 310 c are inputted into a static source error correction (SSEC)module 320. It should be noted that the SSEC module 320 may beimplemented within and/or executed by at least one processor 170 on thevehicle 110. The SSEC module 320 performs source correction on thesensed data 310 a, 310 b, 310 c by removing known corruption effectsfrom the sensed data 310 a, 310 b, 310 c to generate clean datameasurements 325 a, 325 b, 325 c. The SSEC module 320 removes the knowncorruption effects from the sensed data 310 a, 310 b, 310 c by utilizingother data measurements including, but not limited to, dynamic pressure,Mach number, angle of attack (ADA), flap position, gear position, excessthrust, and/or ground effect terms.

Once the known corruption effects from the sensed data 310 a, 310 b, 310c have been removed, the clean data measurements 325 a, 325 b, 325 c areinputted into source selection fault detection (SSFD) modules 330 a, 330b, 330 c. The SSFD modules 330 a, 330 b, 330 c may be implemented withinand/or executed by at least one processor 170 on the vehicle 110. EachSSFD module 330 a, 330 b, 330 c performs majority voting on the inputtedclean data measurements 325 a, 325 b, 325 c to generate a single votedvalue 350 a, 350 b, 350 c. For example, SSFD module 330 a performsmajority voting on three clean data measurements 325 a to generate asingle voted value 350 a.

Also, during operation, the acceleration (A_(N)) data 310 d is inputtedinto a fault management module 340. The fault management module 340 maybe implemented within and/or executed by at least one processor 170 onthe vehicle 110. The fault management module 340 performs majorityvoting on the acceleration (A_(N)) data 310 d to generate a single votedvalue 350 d.

It should be noted that, in one or more embodiments, various differenttypes of majority voting algorithms (e.g., the Boyer-Moore majorityvoting algorithm) may be utilized by the SSFD modules 330 a, 330 b, 330c and the fault management module 340 to perform the majority voting.

After all of the single voted values 350 a, 350 b, 350 c, 350 d havebeen generated, the single voted values 350 a, 350 b, 350 c, 350 d areinputted into a synthetic air data and common mode monitor (CMM) 360.The synthetic air data and CMM 360 may be implemented within and/orexecuted by at least one processor 170 on the vehicle 110. The syntheticair data and CMM 360 utilizes the single voted values 350 a, 350 b, 350c, 350 d to generate trusted values 370 a, 370 b, 370 c, 370 d to beused (e.g., to be used by the flight control system) for operation ofthe vehicle 110. In particular, the synthetic air data and CMM 360utilizes the single voted values 350 a, 350 b, 350 c, 350 d to generatea total pressure (P_(T)) trusted value 370 a, a total pressure (P_(S))trusted value 370 b, an AOA trusted value 370 c, and an accelerationtrusted value 370 d. Details of the operation of the synthetic air dataand CMM 360 will be described in the description of FIGS. 4, 5, 8, 9,and 10.

Referring back to FIG. 3, after the trusted values 370 a, 370 b, 370 c,370 d have been generated by the synthetic air data and CMM 360, thetrusted values 370 a, 370 b, 370 c, 370 d are inputted into an air datacomputations module 380. The air data computations module 380 may beimplemented within and/or executed by at least one processor 170 (whichmay be part of the flight control system) on the vehicle 110. The airdata computations module 380 may utilize the trusted values 370 a, 370b, 370 c, 370 d for making calculations needed for the operation (e.g.,flight) of the vehicle (e.g., airplane) 110.

FIG. 4 is a block diagram 400 showing details of the functionality ofthe synthetic air data and common mode monitor (CMM) 360 of FIG. 3, inaccordance with at least one embodiment of the present disclosure. Inthis figure, the single voted values 350 a, 350 b, 350 c, 350 d areinputted into the synthetic air data and CMM 360. In particular, thevoted value for total pressure (P_(T)) 350 a, the voted value for staticpressure (P_(S)) 350 b, the voted value for AOA 350 c, and the votedvalue for acceleration 350 d are inputted into the synthetic air dataand CMM 360.

After the synthetic air data and CMM 360 has received the single votedvalues 350 a, 350 b, 350 c, 350 d, an estimator 410 a, 410 b, 410 c, 410d for each of the types of data generates estimated values (e.g.,synthetic values) 420 a, 420 b, 420 c, 420 d using some of the singlevoted values 350 a, 350 b, 350 c, 350 d. In particular, each estimator410 a, 410 b, 410 c, 410 d, for a type of data uses the single votedvalues 350 a, 350 b, 350 c, 350 d for the other types of data togenerate estimated values 420 a, 420 b, 420 c, 420 d for all of thetypes of data.

The estimators 410 a, 410 b, 410 c, 410 d use statistical filters togenerate the estimated values 420 a, 420 b, 420 c, 420 d. Variousdifferent types of statistical filters may be used by the estimators 410a, 410 b, 410 c, 410 d to generate the estimated values 420 a, 420 b,420 c, 420 d including, but not limited to, Kalman filters, extendedKalman filters (EKFs), unscented Kalman filters, frequency-weightedKalman filters, fixed-lag smoothers, fixed-interval smoothers (e.g.,Rauch-Tung-Striebel smoothers, modified Bryson-Frazier smoothers, andminimum-variance smoothers), Kalman-Bucy filters, and hybrid Kalmanfilters.

For example, estimator 1 410 a for the total pressure (PT) data type,utilizes the voted value for static pressure (P_(S)) 350 b, the votedvalue for AOA 350 c, and the voted value for acceleration 350 d togenerate the estimated values 420 a (which include an estimated valuefor total pressure (PT), an estimated value for static pressure (P_(S)),an estimated value for AOA, and an estimated value for acceleration).Also, estimator 2 410 b for the static pressure (P_(S)) data type,utilizes the voted value for total pressure (P_(T)) 350 a, the votedvalue for AOA 350 c, and the voted value for acceleration 350 d togenerate the estimated values 420 b (which include an estimated valuefor total pressure (PT), an estimated value for static pressure (P_(S)),an estimated value for AOA, and an estimated value for acceleration). Inaddition, estimator 3 410 c for the AOA data type, utilizes the votedvalue for total pressure (P_(T)) 350 a, the voted value for staticpressure (P_(S)) 350 b, and the voted value for acceleration 350 d togenerate the estimated values 420 c (which include an estimated valuefor total pressure (P_(T)), an estimated value for static pressure(P_(S)), an estimated value for AOA, and an estimated value foracceleration). And, estimator 4 410 d for the acceleration data type,utilizes the voted value for total pressure (P_(T)) 350 a, the votedvalue for static pressure (P_(S)) 350 b, and the voted value for AOA 350c to generate the estimated values 420 d (which include an estimatedvalue for total pressure (PT), an estimated value for static pressure(P_(S)), an estimated value for AOA, and an estimated value foracceleration).

After the estimators 410 a, 410 b, 410 c, 410 d have each generatedestimated values 420 a, 420 b, 420 c, 420 d, the estimated values 420 a,420 b, 420 c, 420 d are inputted into a fault detection and isolationlogic module 430. The fault detection and isolation logic module 430generates residuals (r₁, r₂, r₃, r₄) by comparing the estimated values420 a, 420 b, 420 c, 420 d to the voted values 350 a, 350 b, 350 c, 350d. Details of how the residuals are generated are described in thedescription of FIG. 9. After the residuals (r₁, r₂, r₃, r₄) aregenerated, the fault detection and isolation logic module 430 analyzes apattern of the residuals (r₁, r₂, r₃, r₄) to determine which (if any)type of the data is erroneous. An exemplary fault detection logic (i.e.logic for detecting an erroneous type of data) is shown in FIG. 8, andan exemplary expected pattern of residuals is shown in FIG. 10.

After the fault detection and isolation logic module 430 has analyzedthe pattern of residuals (r₁, r₂, r₃, r₄), if the fault detection andisolation logic module 430 determines that there is no erroneous data,the fault detection and isolation logic module 430 will simply outputthe voted values 350 a, 350 b, 350 c, 350 d to be used for the trustedvalues 370 a, 370 b, 370 c, 370 d (e.g., total pressure (P_(T)) trustedvalue 370 a, total pressure (P_(S)) trusted value 370 b, AOA trustedvalue 370 c, and acceleration trusted value 370 d).

However, if the fault detection and isolation logic module 430determines that a type of data (e.g., total pressure (P_(T))) iserroneous, the fault detection and isolation logic module 430 willoutput the voted values 350 b, 350 c, 350 d for the other types of datato be used for the trusted values 370 b, 370 c, 370 d (e.g., totalpressure (P_(S)) trusted value 370 b, AOA trusted value 370 c, andacceleration trusted value 370 d) for those other types of data. And,the fault detection and isolation logic module 430 will output theestimated value (synthetic data) 420 a generated by the data type'sassociated estimator (e.g., estimator 1) 420 a for that erroneous datatype (e.g., total pressure (P_(T))) to be used for the trusted value 370a (e.g., total pressure (P_(T)) trusted value) for that erroneous datatype.

FIG. 5 shows equations for generating the estimated values utilized bythe disclosed system for sensor fault detection and identification for avehicle 110, in accordance with at least one embodiment of the presentdisclosure. For these equations, x is a vector representing theunderlying state for the data types, and y is a vector representing thevoted values 350 a, 350 b, 350 c, 350 d for the data types. Also, each ŷis a vector representing the estimated values 420 a, 420 b, 420 c, 420 dgenerated by each of the estimators 410 a, 410 b, 410 c, 410 d. Inparticular, for these equations, ŷ₁ is the estimated values generated byestimator 1 410 a, ŷ₂ is the estimated values generated by estimator 2410 b, ŷ₃ is the estimated values generated by estimator 3 410 c, and ŷ₄is the estimated values generated by estimator 4 410 d. Also, for theseequations, f (e.g., f₁, f₂, f₃, and f₄) represents the statisticalfilter (e.g., extended Kalman filter (EKF)) used to generate theestimated values 420 a, 420 b, 420 c, 420 d. And, the variables (e.g.,P_(T), P_(S), α, A_(N)) within the parenthesis represent the votedvalues 350 a, 350 b, 350 c, 350 d used to generate the estimated values420 a, 420 b, 420 c, 420 d.

For example, for estimator 1 410 a (which is associated with the totalpressure (P_(T)) data type), the voted value for static pressure (P_(S))350 b, the voted value for AOA (α) 350 c, and the voted value foracceleration 350 d are used by the statistical filter (f₁) (e.g., EKF)to generate the estimated values 420 a. As such, when there are m numberof different data types sensed, each estimator 410 a, 410 b, 410 c, 410d will use n number (which is equal to m minus 1 (n=m−1)) of votedvalues 350 a, 350 b, 350 c, 350 d to generate the estimated values 420a, 420 b, 420 c, 420 d.

FIG. 6A shows exemplary graphs 600 illustrating the sensed data (i.e.measured data) 310 a, 310 b, 310 c, 310 d and the corresponding realvalues (i.e. truth values) for four different types of sensor data,where none of the sensed data 310 a, 310 b, 310 c, 310 d is erroneous,in accordance with at least one embodiment of the present disclosure.Each graph of this figure shows a different type of sensed data (e.g.,total pressure (P_(T)), static pressure (P_(S)), AOA, and acceleration).As is shown in this figure, the sensed data (i.e. measured values) 310a, 310 b, 310 c, 310 d closely follow the real values (i.e. truthvalues) for that data and, as such, no type of data is erroneous.

FIG. 6B shows exemplary graphs 610 illustrating the estimated values(i.e. synthetic values) 420 a, 420 b, 420 c, 420 d generated by thedisclosed system for sensor fault detection and identification for avehicle 110, where none of the sensed data 310 a, 310 b, 310 c, 310 d iserroneous, in accordance with at least one embodiment of the presentdisclosure. In this figure, each row of graphs illustrates the estimatedvalues 420 a, 420 b, 420 c, 420 d generated by one of the estimators 410a, 410 b, 410 c, 410 d. For example, the first row of graphs shows theestimated values 420 a generated by estimator 1 410 a, the second row ofgraphs shows the estimated values 420 b generated by estimator 2 410 b,the third row of graphs shows the estimated values 420 c generated byestimator 3 410 c, and the fourth row of graphs shows the estimatedvalues 420 d generated by estimator 4 410 d.

In FIG. 6B, each of the sixteen graphs shows four lines of data, one ofthe middle lines of data of each graph is the generated estimated value420 a, 420 b, 420 c, 420 d for the data type of the graph. The othermiddle lines of data of each graph are the sensed data (i.e. measuredvalues) 310 a, 310 b, 310 c, 310 d. The upper and lower lines of data ofeach graph denote an upper and lower threshold (T) for the estimateddata (e.g., estimated values) 420 a, 420 b, 420 c, 420 d to lie within.

FIG. 7A shows exemplary graphs illustrating the sensed data (i.e.measured data) 310 a, 310 b, 310 c, 310 d and the corresponding realvalues (i.e. truth values) for four different types of sensor data,where one of the types of sensed data (i.e. total pressure (P_(T))) 310a is erroneous, in accordance with at least one embodiment of thepresent disclosure. Each graph of this figure shows a different type ofsensed data (e.g., total pressure (P_(T)), static pressure (P_(S)), AOA,and acceleration). As is shown in this figure, the sensed data (i.e.measured values) for static pressure (P_(S)), AOA, and acceleration 310b, 310 c, 310 d, closely follow the real values (i.e. truth values) forthat data. However, the sensed data (i.e. measured values) for totalpressure (P_(T)) 310 a does not closely follow the real values (i.e.truth values) for that data type and, thus, the sensed data for totalpressure (P_(T)) 310 a is erroneous. As such, it can be inferred thatthe pitot tubes 120, which sense the total pressure (P_(T)), areexperiencing a failure (e.g., a CMPE).

FIG. 7B shows exemplary graphs illustrating the estimated values (i.e.synthetic values) 420 a, 420 b, 420 c, 420 d generated by the disclosedsystem for sensor fault detection and identification for a vehicle 110,where one of the types of sensed data (i.e. total pressure (P_(T))) 310a is erroneous, in accordance with at least one embodiment of thepresent disclosure. In this figure, each row of graphs illustrates theestimated values 420 a, 420 b, 420 c, 420 d generated by one of theestimators 410 a, 410 b, 410 c, 410 d. For example, the first row ofgraphs shows the estimated values 420 a generated by estimator 1 410 a,the second row of graphs shows the estimated values 420 b generated byestimator 2 410 b, the third row of graphs shows the estimated values420 c generated by estimator 3 410 c, and the fourth row of graphs showsthe estimated values 420 d generated by estimator 4 410 d.

In FIG. 7B, each of the sixteen graphs shows four lines of data, one ofthe middle lines of data of each graph is the generated estimated value420 a, 420 b, 420 c, 420 d for the data type of the graph. The othermiddle lines of data of each graph are the sensed data (i.e. measuredvalues) 310 a, 310 b, 310 c, 310 d. The upper and lower lines of data ofeach graph denote an upper and lower threshold (T) for the estimatedvalue (e.g., estimated data) 420 a, 420 b, 420 c, 420 d to lie within.

In this figure, the graphs for the total pressure (P_(T)) data type(refer to the first column of graphs) exhibit that the sensed totalpressure (P_(T)) 310 a lies outside of the lower threshold (T). Itshould be noted that since the sensed data for total pressure (P_(T))310 a is erroneous, the estimated value 420 a generated by estimator 1420 a will be used as the trusted value for total pressure (P_(T)) 470a.

FIG. 8 shows equations for the fault detection logic utilized by thedisclosed system for sensor fault detection and identification for avehicle 110, in accordance with at least one embodiment of the presentdisclosure. In this figure, the fault detection logic states that asensed data type is erroneous (i.e. and the associated type of sensorhas failed) if and only if (IFF): (1) the residuals produced from theestimator associated with that data type are small, and (2) theresiduals produced from the estimators associated with the other datatypes are large. For example, the sensed data for total pressure (P_(T))310 a is erroneous (i.e. and the pitot tubes 120 have failed), if andonly if: (1) the residuals (r₁) produced from estimator 1 410 a aresmall (i.e. |r_(1,Ps)|<T and |r_(1,α)|<T and |r_(2,AN)|<T), and theresiduals produced from estimator 2 410 b, estimator 3 410 c, andestimator 4 410 d are large (i.e. |r_(2,PT)|≥T and |r_(3,PT)|≥T and|r_(4,PT)|≥T). It should be noted that T represents a predeterminedthreshold. Also, it should be noted that, in one or more embodiments,the different threshold T values may be used for the differentresiduals.

Also, it should be noted that the residuals produced from estimator 1410 a are small because estimator 1 410 a does not utilize the erroneoussensed data for total pressure (PT) 310 a to generate its estimatedvalues 420 a, which is used to calculate the residuals (r₁). And, theresiduals produced from estimator 2 410 b, estimator 3 410 c, andestimator 4 410 d are large because these estimators 410 b, 410 c, 410 dutilize the erroneous sensed data for total pressure (P_(T)) 310 a togenerate their estimated values 420 b, 420 c, 420 d, which are used tocalculate the residuals (r₂, r₃, r₄).

FIG. 9 is a diagram 900 showing how the residuals are generated by thedisclosed system for sensor fault detection and identification for avehicle 110, in accordance with at least one embodiment of the presentdisclosure. It should be noted that the example shown in this figure isusing a total of three types of data (e.g., total pressure (P_(T)),static pressure (PS), and AOA). In this figure, each estimator (e.g.,observer (OBS) 1, OBS 2, and OBS 3) 910 a, 910 b, 910 c, is using votedvalues from two of the data types to generate estimated values for allthree of the data types.

For example, the first estimator (OBS 1) 910 a, which is associated withthe total pressure (P_(T)) data type, utilizes static pressure (P_(S))voted value (y₂) and AOA voted value (y₃) to generate (by using astatistical filter f₁) its estimated value ŷ₁. Also, the secondestimator (OBS 2) 910 b, which is associated with the static pressure(P_(S)) data type, utilizes total pressure (P_(T)) voted value (y₁) andAOA voted value (y₃) to generate (by using a statistical filter f₂) itsestimated value ŷ₂. And, the third estimator (OBS 3) 910 c, which isassociated with the AOA data type, utilizes total pressure (P_(T)) votedvalue (y₁) and static pressure (P_(S)) voted value (y₂) to generate (byusing a statistical filter f₃) its estimated value ŷ₃.

After the estimated values ŷ₁, ŷ₂, ŷ₃ are generated by the estimators910 a, 910 b, 910 c, the voted values y₁, y₂, y₃ are subtracted fromtheir respective estimated values ŷ₁, ŷ₂, ŷ₃ to generate the residualsr₁, r₂, r₃. As such, r₁ is equal to ŷ₁-y₁; r₂ is equal to ŷ₂-y₂; and r₃is equal to ŷ₃-y₃.

FIG. 10 is a chart 1000 showing an exemplary expected pattern ofresiduals utilized by the disclosed system for sensor fault detectionand identification for a vehicle 110, in accordance with at least oneembodiment of the present disclosure. Similar to FIG. 9, the exampleshown in this figure is using a total of three types of data (e.g.,total pressure (P_(T)), static pressure (PS), and AOA). In this figure,the first three rows 1010 of the chart 1000 correspond to the firstresidual r₁, the second three rows 1020 of the chart 1000 correspond tothe second residual r₂, and the last three rows 1030 of the chart 1000correspond to the third residual r₃.

The second column of the chart 1000 (f₁=f₂=f₃=0) shows the expectedpattern of residuals for when no type of data is erroneous. Since noneof the data is erroneous, all of the residuals should be close to zero,as is shown in the second column.

The third column of the chart 1000 (f₁≠0, f₂=0, f₃=0) shows the expectedpattern of residuals when the sensed data for the first data type (e.g.,total pressure (P_(T))) is erroneous. The residuals r₂, r₃ (refer to therows of 1020 and 1030) for the second and third data types will not beclose to zero because their associated estimated values ŷ₂, ŷ₃ weregenerated using the voted value y₁ for the first data type (e.g., totalpressure (P_(T))).

The fourth column of the chart 1000 (f₁=0, f₂≠0, f₃=0) shows theexpected pattern of residuals when the sensed data for the second datatype (e.g., static pressure (P_(S))) is erroneous. The residuals r₁, r₃(refer to the rows of 1010 and 1030) for the first and third data typeswill not be close to zero because their associated estimated values ŷ₁,ŷ₃ were generated using the voted value y₂ for the second data type(e.g., static pressure (P_(S))).

The fifth column of the chart 1000 (f₁=0, f₂=0, f₃≠0) shows the expectedpattern of residuals when the sensed data for the third data type (e.g.,AOA) is erroneous. The residuals r₁, r₂ (refer to the rows of 1010 and1020) for the first and second data types will not be close to zerobecause their associated estimated values ŷ₁, ŷ₂ were generated usingthe voted value y₃ for the third data type (e.g., AOA).

FIGS. 11A and 11B together form a flow chart showing the disclosedmethod for sensor fault detection and identification for a vehicle, inaccordance with at least one embodiment of the present disclosure. Atthe start 1110 of the method, sensors located on the vehicle sense data(e.g., measurement data) 1120. In one or more embodiments, the datacomprises m number types of data, and m is an integer greater than two.Then, at least one processor removes known corruption effects from atleast some of the data 1130. At least one processor then performedmajority voting on the data for each of the types of data to generate asingle voted value for each of the types of data 1140.

Then, at least one processor generates, for each of the types of data, mnumber of estimated values by using n number of the voted values 1150.In one or more embodiments, n is equal to m minus 1 (i.e. n=m−1). Atleast one processor then generates residuals by comparing the estimatedvalues to the voted values 1160. Then, at least one processor analyzes apattern of the residuals to determine which of the types of the data iserroneous to detect and identify a fault experienced by at least one ofthe sensors on the vehicle 1170.

Then, at least one processor generates an alert signal indicating whichof the types of the data is erroneous and/or which type of the sensorare experiencing a failure 1180. A visual alert (e.g., using anindicator light and/or a display) and/or an audible alert (e.g., using aspeaker) is then generated based on information contained in the alertsignal 1190. Then, a synthetic data signal is generated that indicatesto use synthetic data instead of erroneous sensed data 1195. Then, themethod ends 1197.

Although particular embodiments have been shown and described, it shouldbe understood that the above discussion is not intended to limit thescope of these embodiments. While embodiments and variations of the manyaspects of the invention have been disclosed and described herein, suchdisclosure is provided for purposes of explanation and illustrationonly. Thus, various changes and modifications may be made withoutdeparting from the scope of the claims.

Where methods described above indicate certain events occurring incertain order, those of ordinary skill in the art having the benefit ofthis disclosure would recognize that the ordering may, be modified andthat such modifications are in accordance with the variations of thepresent disclosure. Additionally, parts of methods may be performedconcurrently in a parallel process when possible, as well as performedsequentially. In addition, more steps or less steps of the methods maybe performed.

Accordingly, embodiments are intended to exemplify alternatives,modifications, and equivalents that may fall within the scope of theclaims.

Although certain illustrative embodiments and methods have beendisclosed herein, it can be apparent from the foregoing disclosure tothose skilled in the art that variations and modifications of suchembodiments and methods can be made without departing from the truespirit and scope of this disclosure. Many other examples exist, eachdiffering from others in matters of detail only. Accordingly, it isintended that this disclosure be limited only to the extent required bythe appended claims and the rules and principles of applicable law.

We claim:
 1. A method for sensor fault detection and identification fora vehicle, the method comprising: sensing, with sensors located on thevehicle, data, wherein the data comprises m number types of data, and mis an integer greater than two; performing, by at least one processor,majority voting on the data for each of the types of data to generate asingle voted value for each of the types of data; generating, by the atleast one processor, for each of the types of data, m number ofestimated values by using n number of the voted values, wherein n isequal to m minus one; generating, by the at least one processor,residuals by comparing the estimated values to the voted values; andanalyzing, by the at least one processor, a pattern of the residuals todetermine which of the types of the data is erroneous to detect andidentify a fault experienced by at least one of the sensors on thevehicle.
 2. The method of claim 1, wherein the data comprisesmeasurement data.
 3. The method of claim 1, wherein the sensors compriseat least two of a pitot tube, a pitot-static tube, a static port, astatic tube, an angle of attack (AOA) resolver, or an accelerometer. 4.The method of claim 1, wherein the types of data comprise at least threeof total pressure, static pressure, angle of attack (AOA), oracceleration.
 5. The method of claim 1, wherein the vehicle is one of anairborne vehicle, a terrestrial vehicle, or a marine vehicle.
 6. Themethod of claim 5, wherein the airborne vehicle is one of an airplane,an unmanned aerial vehicle (UAV), or a helicopter.
 7. The method ofclaim 1, wherein the method further comprises removing, by the at leastone processor, known corruption effects from at least some of the data.8. The method of claim 7, wherein the known corruption effects areremoved by the at least one processor by utilizing at least one ofdynamic pressure, Mach number, angle of attack (AOA), flap position,gear position, excess thrust, or ground effect terms.
 9. The method ofclaim 1, wherein the at least one processor generates the estimatedvalues by utilizing at least one statistical filter.
 10. The method ofclaim 9, wherein the at least one statistical filter is an extendedKalman filter (EKF).
 11. The method of claim 1, wherein the methodfurther comprises generating, by the at least one processor, an alertsignal indicating at least one of which of the types of data iserroneous or which type of the sensors are experiencing a failure. 12.The method of claim 11, wherein the method further comprises generatingat least one of a visual alert or an audible alert based on informationcontained in the alert signal.
 13. The method of claim 1, wherein themethod further comprises generating, by the at least one processor, asynthetic data signal indicating to use synthetic data instead oferroneous sensed data.
 14. A system for sensor fault detection andidentification for a vehicle, the system comprising: sensors, located onthe vehicle, to sense data, wherein the data comprises m number types ofdata, and m is an integer greater than two; and at least one processorto perform majority voting on the data for each of the types of data togenerate a single voted value for each of the types of data, to generatefor each of the types of data, m number of estimated values by using nnumber of the voted values, wherein n is equal to m minus one, togenerate residuals by comparing the estimated values to the votedvalues, and to analyze a pattern of the residuals to determine which ofthe types of the data is erroneous to detect and identify a faultexperienced by at least one of the sensors on the vehicle.
 15. Thesystem of claim 14, wherein the sensors comprise at least two of a pitottube, a pitot-static tube, a static port, a static tube, an angle ofattack (AOA) resolver, or an accelerometer.
 16. The system of claim 14,wherein the types of data comprise at least three of total pressure,static pressure, angle of attack (AOA), or acceleration.
 17. The systemof claim 14, wherein the at least one processor is further to generatethe estimated values by utilizing at least one statistical filter. 18.The system of claim 14, wherein the at least on processor is further togenerate an alert signal indicating at least one of which of the typesof data is erroneous or which type of the sensors are experiencing afailure.
 19. The system of claim 18, wherein the system furthercomprises at least one of: at least one indicator light to generate avisual alert based on information contained in the alert signal; atleast one display to display a visual display alert based on theinformation contained in the alert signal; or at least one speaker togenerate an audio alert based on the information contained in the alertsignal.
 20. The system of claim 14, wherein the at least one processoris further to generate a synthetic data signal indicating to usesynthetic data instead of erroneous sensed data.